New research provides evidence that public Facebook activity can be effectively used to predict who an individual will vote for. The results suggest that Facebook-based models may be more accurate than conventional methods of predicting voting behavior.

“I was mainly interested in showing a strong connection between online and offline behavior in a political context, because it can serve as a basis for large-scale studies about how people discuss political issues online,” said study author Jakob Bæk Kristensen of the University of Canterbury.

“Building on the evidence in this study we will be able to further study how people reach agreement, change their opinion or how they mingle and congregate differently based on their political preferences.”

The study, published in the scientific journal PLOS One, used the Facebook Graph API to collect political data from January 2015 to January 2017. The data was collected from 378 public Facebook pages of Danish parties and politicians.

The researchers found that they could use Facebook “likes” to create models of voter intention that accurately predicted which of the nine parties in the Danish parliament a given person would vote for. They compared their Facebook prediction models to a more traditional model that examined sociodemographic information, political values, and opinions on political issues.

“The main point is simple: that there is a strong connection between the posts that users like on Facebook and their voter intention,” Kristensen told PsyPost. “Whether this should be interpreted negatively as an increased form of surveillance of citizens or as something that might sustain a more active and democratic culture, will most likely depend on the individual.”

Surprisingly, they found a person’s single latest political “like” was a better indication of their voting intention that the traditional model using sociodemographic and political data. Their prediction model became even more accurate when including individuals’ entire political “like” history.

Combining the traditional model with the Facebook model only resulted in a small increase in accuracy. Their best model correctly predicted the vote of approximately 70 percent of the participants.

“Like most social media platforms Facebook is continuously changing,” Kristensen told PsyPost. “Both its software and its users are constantly creating new practices, thus the results might not apply later down the road. Also, since the results are obtained by analyzing only a single nation, it cannot be automatically assumed that the connection will have the same strength in other countries.

“Another concern is whether the connection between likes and voter intention is strong enough to be able to predict an election, which is a question to be answered in future studies.”

“I personally think it is important to get this confirmation that the online and offline in this respect are not separate worlds, but actually mirror each other quite strongly,” Kristensen added. “But I also think that the important questions are the ones that come next: how can we use this to extend our knowledge about citizens, public opinion and politics?”